PowerZooJax speeds up power system tests using GPUs
PowerZooJax: A JAX-based Power System Benchmark for Reinforcement Learning
Artificial IntelligenceMachine Learning
Summary
Managing power systems safely is a complex job that involves many decisions over time, making it a good fit for reinforcement learning (RL). Existing computer simulations of power systems are slow and limited to CPUs, which makes testing faster or larger scale methods hard. The authors created PowerZooJax, a tool that uses modern GPU technology to run power system tests much faster by rewriting key tasks as GPU-friendly computations. They show it runs simulations much quicker than older CPU-based methods and can test how well RL strategies perform under different conditions. This tool is open for others to use and build upon.
What this means in practice
- •For power system operators: Evaluate and improve real-time decision policies for power grid management with faster and more comprehensive simulation testing on GPUs.
- •For energy software developers: Build advanced reinforcement learning tools for generation, transmission, and microgrid control benefiting from PowerZooJax’s GPU-accelerated simulation framework.
Authors
Zhanhua Pan, Xiao Liu, Zhilong Cao, Jianhong Wang, Dawei Qiu
Abstract
Power system operation is a safety-critical sequential decision-making problem, making it a natural testbed for reinforcement learning (RL). However, existing RL environments for power systems are often narrow in scope and computationally limited by CPU-based simulation workflows, making large-scale evaluation difficult. We introduce PowerZooJax, a JAX-based benchmark suite for RL in power system operation. It provides five constrained Markov decision process tasks spanning generation, transmission, distribution, distributed energy resources, and data center microgrid. By rewriting power flow, economic dispatch, market clearing, and device dynamics as JAX computation graphs, PowerZooJax keeps the entire training and evaluation loop on the GPU. Experiments show substantial speedups over CPU-based simulations and demonstrate standardized evaluation of policy returns, safety violations, and out-of-distribution stress conditions. Our open-source benchmark is available at: https://github.com/powerzoojax/PowerZooJax.